Using LiDAR and remote microclimate loggers to downscale near-surface air temperatures for site-level studies
2015
George, Andrew D. | Thompson, Frank R. | Faaborg, John
A spatial mismatch exists between regional climate models and conditions experienced by individual organisms. We demonstrate an approach to downscaling air temperatures for site-level studies using airborne LiDAR data and remote microclimate loggers. In 2012–2013, we established a temperature logger network in the forested region of central Missouri, USA, and obtained sub-hourly meteorological measurements from a centrally located weather station. We then used linear mixed models within an information theoretic approach to evaluate hourly and seasonal effects of insolation, vegetation structure, elevation, and meteorological measurements on near-surface air temperatures. The best-supported models predicted fine-scale temperatures with high accuracy during both the winter and growing seasons. We recommend that researchers consider the scales relevant to specific applications when using our approach to develop site-specific spatio-temporal models.
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